In the user reviews of various domains, there is an increase in the accumulation of reviews in the web that presents a lot of difficulties to the readers. So it becomes necessary to generate a summary which represents the entire review in a concise manner. It is required for each feature or aspect in the reviews for the ease of users. The aspect-based summarisation plays a vital role in the field of opinion mining. This article proposes an aspect summarisation framework using sentence scoring clustering and weight-based single-objective optimisation technique by utilising evolutionary algorithm. The system uses MapReduce framework to incorporate the proposed combiner–based optimised clustering approach. Then a novel single-objective optimisation with genetic algorithm is developed. Its purpose is to retrieve top sentences from each cluster to generate feature-based summary. The accuracy of the system-generated summary is evaluated using the Recall Oriented Understanding for Gisting Evaluation tool kit using human standard reference summaries. The system is able to achieve more promising results when compared with other standard feature–based summarisation systems.

中文翻译：

---

使用分布式聚类和单目标优化的基于方面的摘要

在各个领域的用户评论中，网络评论的积累不断增加，给读者带来了很多困难。因此，有必要生成一个摘要，以简洁的方式代表整个评论。为了方便用户，评论中的每个功能或方面都需要它。基于方面的摘要在意见挖掘领域起着至关重要的作用。本文提出了一种基于句子评分聚类和基于权重的单目标优化技术的方面总结框架，利用进化算法。该系统使用 MapReduce 框架来合并提出的基于组合器的优化聚类方法。然后开发了一种新的遗传算法单目标优化。其目的是从每个集群中检索最热门的句子以生成基于特征的摘要。系统生成的摘要的准确性使用人类标准参考摘要使用 Recall Oriented Understanding for Gisting Evaluation 工具包进行评估。与其他基于特征的标准摘要系统相比，该系统能够取得更有希望的结果。